{"product_id":"applied-recommender-systems-with-python-9781484289532","title":"Applied Recommender Systems with Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.\u003c\/p\u003e\u003cp\u003eYou''ll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Ea\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1: Introduction to Recommender SystemsChapter Goal: Introduction of recommender systems, along with a high-level overview of how recommender systems work, what are the different existing types, and how to leverage basic and advanced machine learning techniques to build these systems.No of pages: 25Sub - Topics:\t \u003cbr\u003e1.\tIntro to recommender system 2.\tHow it works3.\tTypes and how they worka.\tAssociation rule miningb.\tContent basedc.\tCollaborative filtering d.\tHybrid systemse.\tML Clustering basedf.\tML Classification basedg.\tDeep learning and NLP basedh.\tGraph based\u003cbr\u003eChapter 2: Association Rule MiningChapter Goal: Building one of the simplest recommender systems from scratch, using association rule mining; also called market basket analysis.No of pages: 20Sub - Topics\t1\tAPRIORI2\tFP GROWTH3\tAdvantages and Disadvantages\u003cbr\u003eChapter 3: Content and Knowledge-Based Recommender SystemChapter Goal: Building the content and knowledge-based recommender system from scratch using both product content and demographicsNo of pages: 25Sub - Topics\t1\tTF-IDF2\tBOW3\tTransformer based4\tAdvantages and disadvantages\u003cbr\u003e\u003cbr\u003eChapter 4: Collaborative Filtering using KNNChapter Goal: Building the collaborative filtering using KNN from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics: 1\tKNN – item based2\tKNN – user based3\tAdvantages and disadvantages\u003cbr\u003e\u003cbr\u003eChapter 5: Collaborative Filtering Using Matrix Factorization, SVD and ALS.Chapter Goal: Building the collaborative filtering using SVM from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics: 1\tLatent factors2\tSVD3\tALS4\tAdvantages and disadvantages\u003cbr\u003e\u003cbr\u003eChapter 6: Hybrid Recommender SystemChapter Goal: Building the hybrid recommender system (Using both content and collaborative methods) which is widely used in the industryNo of pages: 25Sub - Topics: 1\tWeighted: a different weight given to the recommenders of each technique used to favor some of them.2\tMixed: a single set of recommenders, without favorites.3\tAugmented: suggestions from one system are used as input for the next, and so on until the last one.4\tSwitching: Choosing a random method5\tAdvantages and disadvantages\u003cbr\u003e\u003cbr\u003eChapter 7: Clustering Algorithm-Based Recommender SystemChapter Goal: Building the clustering model for recommender systems.No of pages: 25Sub - Topics: 1\tK means clustering2\tHierarchal clustering 3\tAdvantages and disadvantages\u003cbr\u003e\u003cbr\u003eChapter 8: Classification Algorithm-Based Recommender SystemChapter Goal: Building the classification model for recommender systems.No of pages: 25Sub - Topics: 1\tBuying propensity model2\tLogistic regression3\tRandom forest4\tSVM5\tAdvantages and disadvantages\u003cbr\u003e\u003cbr\u003eChapter 9: Deep Learning and NLP Based Recommender SystemChapter Goal: Building state of art recommender system using advanced topics like Deep learning along with NLP (Natural Language processing).No of pages: 25Sub - Topics: 1\tWord embedding’s2\tDeep neural networks3\tAdvantages and disadvantages\u003cbr\u003e\u003cbr\u003eChapter 10: Graph-Based Recommender SystemChapter Goal: Implementing graph-based recommender system using Python for computation performanceNo of pages: 25Sub - Topics: 1\tGenerating nodes and edges2\tBuilding algorithm3\tAdvantages and disadvantages\u003cbr\u003e\u003cbr\u003eChapter 11: Emerging Areas and Techniques in Recommender System Chapter Goal: To get an overview of the new and emerging techniques and the areas of research in Recommender systemsNo of pages: 15Sub - Topics: 1\tPersonalized recommendation engine2\tContext-based search engine3\tMulti-objective recommendations4\tSummary\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48885830320471,"sku":"9781484289532","price":29.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484289532.jpg?v=1722537850","url":"https:\/\/bookcurl.com\/products\/applied-recommender-systems-with-python-9781484289532","provider":"Book Curl","version":"1.0","type":"link"}